基于在线自组织增量学习的非侵入式负荷识别方法

Non-intrusive Load Identification Method Based on the Online Self-organizing Incremental Neural Network

随着电子技术智能化的发展,对实现电器负荷使用情况的精准识别在智慧用电领域将有广泛的用户需求。为了实现对电器设备的实时在线精确监测,本文提出了一种基于在线自组织增量学习(SOINN)的非侵入式负荷识别方法。该方法包含负荷特征提取、负荷特征分类及电器识别2个步骤。在负荷特征提取步骤中,提出了包含奇次谐波、均值、方差、3阶矩、4阶矩、电流有效值、功率谱峰值、功率谱谷值在内的共12维特征的特征提取方案。在负荷特征分类及电器识别步骤中,提出了结合SVM的SOINN的负荷特征分类及电器识别方法,以克服传统的SOINN算法不能实现电器类型识别功能的缺陷。通过C++语言将所提方法中的功能算法编写成微处理器系统的可执行功能模块,将功能模块移植部署在SoCFPGA的HPS端运行,实现了FPGA和HPS之间的协同高速数据通信。选取了8种常规家用电器作为负荷识别对象,搭建了基于SoCFPGA的硬件实验平台,进行了最优负荷特征选取,并采用本文方法对单电器与多电器的在线负荷进行了识别。实验结果:选取12维特征为本文方法的最优特征组合;本文方法的单电器与多电器的识别率均在95%以上。本文提出的负荷识别方法能够有效、准确地识别单电器与多电器;系统可实施性强,灵活性高,具有在线学习的优越性与实际应用的切实可行性。

With the development of intelligent electronic technology,the accurate identification of electrical load usage will have extensive user demands in the field of smart electricity.In order to achieve the online real-time accurate monitoring of the electrical equipment,this paper proposed a non-intrusive load identification method based on the online self-organizing incremental neural network(SOINN).This method included two steps,which are the load feature extraction and the load feature classification with the equipment identification.In the process of load feature extraction,a 12-dimensional feature extraction scheme was proposed,which includes the odd harmonics,the mean value,the variance value,the third-order moment,the fourth-order moment,the root mean square current,the peak value of power spectrum,and the trough value of power spectrum.In the second step,a method combining SVM and SOINN for the load feature classification and the electrical equipment identification was proposed to overcome the limitation of the traditional SOINN algorithm in appliance type recognition.The functional algorithms in the proposed method are implemented as executable functional modules for the microprocessor system using C++ programming language.The functional modules were then ported and deployed on the HPS side of the SoC FPGA,achieving collaborative high-speed data communication between FPGA and HPS.Eight types of conventional household appliances were selected as the load identification objects.A hardware experimental platform based on SoC FPGA was built to select the optimal load characteristics.The proposed method was validated for identifying online loads of both single and multiple appliances.Experimental results showed that the above 12-dimensional features were selected as the optimal feature combination for the method proposed in this paper.The recognition rates of both single and multiple appliances using the proposed method were above 95%.The proposed load identification method can effectively and accurately identify both single and multiple electrical appliances.The system has strong implementability,high flexibility,the advantages of online learning,and practical feasibility for practical applications.